{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "241796f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import copy\n",
    "import collections\n",
    "import os\n",
    "import pickle\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import py3Dmol\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem\n",
    "from rdkit.Chem import rdMolAlign\n",
    "from rdkit.Chem import PeriodicTable\n",
    "from rdkit.Chem.Lipinski import RotatableBondSmarts\n",
    "import scipy\n",
    "from scipy import spatial as sci_spatial\n",
    "import torch\n",
    "from tqdm.auto import tqdm\n",
    "import seaborn as sns\n",
    "\n",
    "ptable = Chem.GetPeriodicTable()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fe7bae18",
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit import RDLogger\n",
    "RDLogger.DisableLog('rdApp.*')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "33fd17ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys, os\n",
    "sys.path.insert(0, os.path.abspath('../'))\n",
    "\n",
    "from utils.evaluation import eval_bond_length, scoring_func, similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "09665f85",
   "metadata": {},
   "outputs": [],
   "source": [
    "MODEL_NAME = 'TargetDiff'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee6b9e57",
   "metadata": {},
   "source": [
    "# Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8f583ed1",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Globals:\n",
    "    reference_path = 'sampling_results/crossdocked_test_vina_docked.pt'\n",
    "    cvae_path = 'sampling_results/CVAE_test_docked_sf1.5.pt'\n",
    "    ar_path = 'sampling_results/ar_vina_docked.pt'\n",
    "    pocket2mol_path = 'sampling_results/pocket2mol_vina_docked.pt'\n",
    "    our_path = 'sampling_results/targetdiff_vina_docked.pt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "23666e61",
   "metadata": {},
   "outputs": [],
   "source": [
    "Globals.reference_results = torch.load(Globals.reference_path)\n",
    "Globals.reference_results = [[v] for v in Globals.reference_results]\n",
    "Globals.ar_results = torch.load(Globals.ar_path)\n",
    "Globals.pocket2mol_results = torch.load(Globals.pocket2mol_path)\n",
    "Globals.cvae_results = torch.load(Globals.cvae_path)\n",
    "Globals.our_results = torch.load(Globals.our_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52e29805-a409-46a1-b385-66b0ad293b1d",
   "metadata": {},
   "source": [
    "# Metrics Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2684d5c5-73b7-4c6e-ba5c-7abd42eade71",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_results(results, show_vina=True):\n",
    "    qed = [r['chem_results']['qed'] for r in results]\n",
    "    sa = [r['chem_results']['sa'] for r in results]\n",
    "    mol_size = [r['mol'].GetNumAtoms() for r in results]\n",
    "    print('Num results: %d' % len(results))\n",
    "    if show_vina:\n",
    "        vina_score_only = [x['vina']['score_only'][0]['affinity'] for x in results]\n",
    "        vina_min = [x['vina']['minimize'][0]['affinity'] for x in results]\n",
    "        vina_dock = [r['vina']['dock'][0]['affinity'] for r in results]\n",
    "        print('[Vina Score] Avg: %.2f | Med: %.2f' % (np.mean(vina_score_only), np.median(vina_score_only)))\n",
    "        print('[Vina Min]   Avg: %.2f | Med: %.2f' % (np.mean(vina_min), np.median(vina_min)))\n",
    "        print('[Vina Dock]  Avg: %.4f | Med: %.4f' % (np.mean(vina_dock), np.median(vina_dock)))\n",
    "        \n",
    "    print('[QED]  Avg: %.4f | Med: %.4f' % (np.mean(qed), np.median(qed)))\n",
    "    print('[SA]   Avg: %.4f | Med: %.4f' % (np.mean(sa), np.median(sa)))\n",
    "    print('[Size] Avg: %.4f | Med: %.4f' % (np.mean(mol_size), np.median(mol_size)))\n",
    "\n",
    "def compute_high_affinity(vina_ref, results):\n",
    "    percentage_good = []\n",
    "    num_docked = []\n",
    "    qed_good, sa_good = [], []\n",
    "    for i in range(100):\n",
    "        score_ref = vina_ref[i]\n",
    "        pocket_results = [r for r in results[i] if r['vina'] is not None]\n",
    "        if len(pocket_results) < 50:\n",
    "            continue\n",
    "        num_docked.append(len(pocket_results))\n",
    "\n",
    "        scores_gen = []\n",
    "        for docked in pocket_results:\n",
    "            aff = docked['vina']['dock'][0]['affinity']\n",
    "            scores_gen.append(aff)\n",
    "            if aff <= score_ref:\n",
    "                qed_good.append(docked['chem_results']['qed'])\n",
    "                sa_good.append(docked['chem_results']['sa'])\n",
    "        scores_gen = np.array(scores_gen)\n",
    "        percentage_good.append((scores_gen <= score_ref).mean())\n",
    "\n",
    "    percentage_good = np.array(percentage_good)\n",
    "    num_docked = np.array(num_docked)\n",
    "\n",
    "    print('[HF%%]  Avg: %.2f%% | Med: %.2f%% ' % (np.mean(percentage_good)*100, np.median(percentage_good)*100))\n",
    "    print('[HF-QED]  Avg: %.4f | Med: %.4f ' % (np.mean(qed_good)*100, np.median(qed_good)*100))\n",
    "    print('[HF-SA]   Avg: %.4f | Med: %.4f ' % (np.mean(sa_good)*100, np.median(sa_good)*100))\n",
    "    print('[Success%%] %.2f%% ' % (np.mean(percentage_good > 0)*100, ))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79832e56-d7c0-491e-a0a8-ffd26835e977",
   "metadata": {},
   "source": [
    "## Reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "71d33554-079d-4065-ab5c-d4216e27d669",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num results: 100\n",
      "[Vina Score] Avg: -6.36 | Med: -6.46\n",
      "[Vina Min]   Avg: -6.71 | Med: -6.49\n",
      "[Vina Dock]  Avg: -7.4501 | Med: -7.2630\n",
      "[QED]  Avg: 0.4760 | Med: 0.4676\n",
      "[SA]   Avg: 0.7277 | Med: 0.7400\n",
      "[Size] Avg: 22.7500 | Med: 21.5000\n"
     ]
    }
   ],
   "source": [
    "flat_ref_docked = [r for pr in Globals.reference_results for r in pr]\n",
    "print_results(flat_ref_docked)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "35e18e63-b7d1-4e0e-8873-ced252c07a44",
   "metadata": {},
   "outputs": [],
   "source": [
    "vina_ref = [r['vina']['dock'][0]['affinity'] for r in flat_ref_docked]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5760f4c2-28f7-4d13-816e-fc08a1ec6fcd",
   "metadata": {},
   "source": [
    "## AR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "66bd61a1-e771-4c6e-89b4-c806e0b9ae17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num results: 9295\n",
      "[Vina Score] Avg: -5.75 | Med: -5.64\n",
      "[Vina Min]   Avg: -6.18 | Med: -5.88\n",
      "[Vina Dock]  Avg: -6.7459 | Med: -6.6220\n",
      "[QED]  Avg: 0.5089 | Med: 0.4997\n",
      "[SA]   Avg: 0.6366 | Med: 0.6300\n",
      "[Size] Avg: 17.6815 | Med: 16.0000\n",
      "[HF%]  Avg: 37.94% | Med: 31.00% \n",
      "[HF-QED]  Avg: 52.1360 | Med: 51.9444 \n",
      "[HF-SA]   Avg: 59.7250 | Med: 59.0000 \n",
      "[Success%] 91.58% \n"
     ]
    }
   ],
   "source": [
    "flat_ar_docked = [r for pr in Globals.ar_results for r in pr]\n",
    "print_results(flat_ar_docked)\n",
    "compute_high_affinity(vina_ref, Globals.ar_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d13e3d8-38c5-4a9a-8fd4-a3231e21058a",
   "metadata": {},
   "source": [
    "## Pocket2Mol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "bf6ec7b2-05be-4210-bc68-4c3c473eb32d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num results: 9831\n",
      "[Vina Score] Avg: -5.14 | Med: -4.70\n",
      "[Vina Min]   Avg: -6.42 | Med: -5.82\n",
      "[Vina Dock]  Avg: -7.1515 | Med: -6.7850\n",
      "[QED]  Avg: 0.5729 | Med: 0.5770\n",
      "[SA]   Avg: 0.7558 | Med: 0.7600\n",
      "[Size] Avg: 17.7363 | Med: 15.0000\n",
      "[HF%]  Avg: 48.36% | Med: 51.00% \n",
      "[HF-QED]  Avg: 56.5935 | Med: 57.5578 \n",
      "[HF-SA]   Avg: 72.3812 | Med: 72.0000 \n",
      "[Success%] 88.78% \n"
     ]
    }
   ],
   "source": [
    "flat_p2m_docked = [r for pr in Globals.pocket2mol_results for r in pr]\n",
    "print_results(flat_p2m_docked)\n",
    "compute_high_affinity(vina_ref, Globals.pocket2mol_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4158b73-f050-410a-a2e8-6e495e2e6d7e",
   "metadata": {},
   "source": [
    "## TargetDiff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c9011e14-0330-4c59-973c-d9ea904031cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num results: 9036\n",
      "[Vina Score] Avg: -5.47 | Med: -6.30\n",
      "[Vina Min]   Avg: -6.64 | Med: -6.83\n",
      "[Vina Dock]  Avg: -7.8018 | Med: -7.9080\n",
      "[QED]  Avg: 0.4798 | Med: 0.4808\n",
      "[SA]   Avg: 0.5846 | Med: 0.5800\n",
      "[Size] Avg: 24.2371 | Med: 24.0000\n",
      "[HF%]  Avg: 58.11% | Med: 59.09% \n",
      "[HF-QED]  Avg: 49.8408 | Med: 49.9754 \n",
      "[HF-SA]   Avg: 56.3709 | Med: 56.0000 \n",
      "[Success%] 98.99% \n"
     ]
    }
   ],
   "source": [
    "flat_ours_docked = [r for pr in Globals.our_results for r in pr]\n",
    "print_results(flat_ours_docked)\n",
    "compute_high_affinity(vina_ref, Globals.our_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6daa192e",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Atom Distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "25889983",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_all_atom_distance(results):\n",
    "    atom_distance_list = []\n",
    "    for pocket in results:\n",
    "        for ligand in pocket:\n",
    "            mol = ligand['mol']\n",
    "            mol = Chem.RemoveAllHs(mol)\n",
    "            pos = mol.GetConformers()[0].GetPositions()\n",
    "            dist = sci_spatial.distance.pdist(pos, metric='euclidean')\n",
    "            atom_distance_list += dist.tolist()\n",
    "    return np.array(atom_distance_list)\n",
    "        \n",
    "Globals.reference_atom_dist = get_all_atom_distance(Globals.reference_results)\n",
    "Globals.our_atom_dist = get_all_atom_distance(Globals.our_results)\n",
    "Globals.ar_atom_dist = get_all_atom_distance(Globals.ar_results)\n",
    "Globals.pocket2mol_atom_dist = get_all_atom_distance(Globals.pocket2mol_results)\n",
    "Globals.cvae_atom_dist = get_all_atom_distance(Globals.cvae_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "aa258849",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_all_c_c_distance(results):\n",
    "    c_c_distance_list = []\n",
    "    for pocket in results:\n",
    "        for ligand in pocket:\n",
    "            mol = ligand['mol']\n",
    "            mol = Chem.RemoveAllHs(mol)\n",
    "            for bond_type, dist in eval_bond_length.bond_distance_from_mol(mol):\n",
    "                if bond_type[:2] == (6, 6):\n",
    "                    c_c_distance_list.append(dist)\n",
    "    return np.array(c_c_distance_list)\n",
    "\n",
    "Globals.reference_c_c_dist = get_all_c_c_distance(Globals.reference_results)\n",
    "Globals.our_c_c_dist = get_all_c_c_distance(Globals.our_results)\n",
    "Globals.ar_c_c_dist = get_all_c_c_distance(Globals.ar_results)\n",
    "Globals.pocket2mol_c_c_dist = get_all_c_c_distance(Globals.pocket2mol_results)\n",
    "Globals.cvae_c_c_dist = get_all_c_c_distance(Globals.cvae_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a7dd8340",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 10))\n",
    "\n",
    "LW = 2\n",
    "LABEL_FONTSIZE = 18\n",
    "ALPHA = 0.75\n",
    "\n",
    "BINS = np.linspace(0, 12, 100)\n",
    "def _plot_other(plot_ylabel=False):\n",
    "    if plot_ylabel:\n",
    "        plt.ylabel('Density', fontsize=LABEL_FONTSIZE)\n",
    "    plt.xlabel('Distance of all atom pairs ($\\AA$)', fontsize=LABEL_FONTSIZE)\n",
    "    plt.ylim(0, 0.5)\n",
    "    plt.xlim(0, 12)\n",
    "    \n",
    "reference_profile = eval_bond_length.get_distribution(Globals.reference_atom_dist, bins=BINS)\n",
    "def _compute_jsd(atom_dist_list):\n",
    "    profile = eval_bond_length.get_distribution(atom_dist_list, bins=BINS)\n",
    "    return sci_spatial.distance.jensenshannon(reference_profile, profile)\n",
    "\n",
    "ax = plt.subplot(2,3,1)\n",
    "plt.hist(Globals.reference_atom_dist, bins=BINS, histtype='step', density=True, lw=LW, color='gray', alpha=ALPHA, label='reference')\n",
    "plt.hist(Globals.cvae_atom_dist, bins=BINS, histtype='step', density=True, lw=LW, color='blue', alpha=ALPHA, label='liGAN')\n",
    "jsd = _compute_jsd(Globals.cvae_atom_dist)\n",
    "ax.text(5, 0.4, f'liGAN JSD: {jsd:.3f}', fontsize=15, weight='bold')\n",
    "# plt.title(f'liGAN (JSD={jsd:.3f})')\n",
    "_plot_other(plot_ylabel=True)\n",
    "ax = plt.subplot(2,3,2)\n",
    "plt.hist(Globals.reference_atom_dist, bins=BINS, histtype='step', density=True, lw=LW, color='gray', alpha=ALPHA, label='reference')\n",
    "plt.hist(Globals.ar_atom_dist, bins=BINS, histtype='step', density=True, lw=LW, color='red', alpha=ALPHA, label='AR')\n",
    "# jsd = _compute_jsd(Globals.ar_atom_dist)\n",
    "plt.hist(Globals.pocket2mol_atom_dist, bins=BINS, histtype='step', density=True, lw=LW, color='orange', alpha=ALPHA, label='Pocket2Mol')\n",
    "ar_jsd = _compute_jsd(Globals.ar_atom_dist)\n",
    "p2m_jsd = _compute_jsd(Globals.pocket2mol_atom_dist)\n",
    "ax.text(4.5, 0.4, f'AR\\nPocket2Mol', fontsize=15, weight='bold')\n",
    "ax.text(8.5, 0.4, f'JSD: {ar_jsd:.3f}\\nJSD: {p2m_jsd:.3f}', fontsize=15, weight='bold')\n",
    "# plt.title(f'AR (JSD={jsd:.3f})')\n",
    "_plot_other()\n",
    "ax = plt.subplot(2,3,3)\n",
    "plt.hist(Globals.reference_atom_dist, bins=BINS, histtype='step', density=True, lw=LW, color='gray', alpha=ALPHA, label='reference')\n",
    "plt.hist(Globals.our_atom_dist, bins=BINS, histtype='step', density=True, lw=LW, color='green', alpha=ALPHA, label=MODEL_NAME)\n",
    "jsd = _compute_jsd(Globals.our_atom_dist)\n",
    "ax.text(5, 0.4, f'{MODEL_NAME} JSD:{jsd:.3f}', fontsize=15, weight='bold')\n",
    "# plt.title(f'{MODEL_NAME} (JSD={jsd:.3f})')\n",
    "_plot_other()\n",
    "\n",
    "BINS = np.linspace(0, 2, 100)\n",
    "\n",
    "def _select_proximal(arr):\n",
    "    arr = np.array()\n",
    "\n",
    "def _plot_other(plot_ylabel=False):\n",
    "    if plot_ylabel:\n",
    "        plt.ylabel('Density', fontsize=LABEL_FONTSIZE)\n",
    "    plt.xlabel('Distance of carbon carbon bond ($\\AA$)', fontsize=LABEL_FONTSIZE)\n",
    "    plt.ylim(0, 12)\n",
    "    plt.xlim(0, 2)\n",
    "    \n",
    "reference_profile = eval_bond_length.get_distribution(Globals.reference_c_c_dist, bins=BINS)\n",
    "def _compute_jsd(atom_dist_list):\n",
    "    profile = eval_bond_length.get_distribution(atom_dist_list, bins=BINS)\n",
    "    return sci_spatial.distance.jensenshannon(reference_profile, profile)\n",
    "\n",
    "ax = plt.subplot(2,3,4)\n",
    "plt.hist(Globals.reference_c_c_dist, bins=BINS, histtype='step', density=True, lw=LW, color='gray', alpha=ALPHA, label='reference')\n",
    "plt.hist(Globals.cvae_c_c_dist, bins=BINS, histtype='step', density=True, lw=LW, color='blue', alpha=ALPHA, label='liGAN')\n",
    "jsd = _compute_jsd(Globals.cvae_c_c_dist)\n",
    "ax.text(0.1, 10, f'liGAN JSD: {jsd:.3f}', fontsize=15, weight='bold')\n",
    "# plt.title(f'liGAN (JSD={jsd:.3f})')\n",
    "_plot_other(plot_ylabel=True)\n",
    "ax = plt.subplot(2,3,5)\n",
    "plt.hist(Globals.reference_c_c_dist, bins=BINS, histtype='step', density=True, lw=LW, color='gray', alpha=ALPHA, label='reference')\n",
    "plt.hist(Globals.ar_c_c_dist, bins=BINS, histtype='step', density=True, lw=LW, color='red', alpha=ALPHA, label='AR')\n",
    "plt.hist(Globals.pocket2mol_c_c_dist, bins=BINS, histtype='step', density=True, lw=LW, color='orange', alpha=ALPHA, label='Pocket2Mol')\n",
    "ar_jsd = _compute_jsd(Globals.ar_c_c_dist)\n",
    "p2m_jsd = _compute_jsd(Globals.pocket2mol_c_c_dist)\n",
    "ax.text(0.1, 10, f'AR\\nPocket2Mol', fontsize=15, weight='bold')\n",
    "ax.text(0.75, 10, f'JSD: {ar_jsd:.3f}\\nJSD: {p2m_jsd:.3f}', fontsize=15, weight='bold')\n",
    "# plt.title(f'AR (JSD={jsd:.3f})')\n",
    "_plot_other()\n",
    "ax = plt.subplot(2,3,6)\n",
    "plt.hist(Globals.reference_c_c_dist, bins=BINS, histtype='step', density=True, lw=LW, color='gray', alpha=ALPHA, label='reference')\n",
    "plt.hist(Globals.our_c_c_dist, bins=BINS, histtype='step', density=True, lw=LW, color='green', alpha=ALPHA, label=MODEL_NAME)\n",
    "jsd = _compute_jsd(Globals.our_c_c_dist)\n",
    "ax.text(0.1, 10, f'{MODEL_NAME} JSD: {jsd:.3f}', fontsize=15, weight='bold')\n",
    "# plt.title(f'{MODEL_NAME} (JSD={jsd:.3f})')\n",
    "_plot_other()\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig('output_figures/distance_distribution.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3355b21",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Bond Distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1f7dddbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_bond_length_profile(results):\n",
    "    bond_distances = []\n",
    "    for pocket in results:\n",
    "        for ligand in pocket:\n",
    "            mol = ligand['mol']\n",
    "            mol = Chem.RemoveAllHs(mol)\n",
    "            bond_distances += eval_bond_length.bond_distance_from_mol(mol)\n",
    "    return eval_bond_length.get_bond_length_profile(bond_distances)\n",
    "\n",
    "Globals.reference_bond_length_profile = get_bond_length_profile(Globals.reference_results)\n",
    "Globals.our_bond_length_profile = get_bond_length_profile(Globals.our_results)\n",
    "Globals.ar_bond_length_profile = get_bond_length_profile(Globals.ar_results)\n",
    "Globals.pocket2mol_bond_length_profile = get_bond_length_profile(Globals.pocket2mol_results)\n",
    "Globals.cvae_bond_length_profile = get_bond_length_profile(Globals.cvae_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "532cd4e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'JSD_6-6|1': 0.36884373598857034,\n",
       " 'JSD_6-6|2': 0.5047684134329029,\n",
       " 'JSD_6-6|4': 0.2633830585480587,\n",
       " 'JSD_6-7|1': 0.3631443132502597,\n",
       " 'JSD_6-7|2': 0.5504188286292794,\n",
       " 'JSD_6-7|4': 0.23528955393614812,\n",
       " 'JSD_6-8|1': 0.4209079543270927,\n",
       " 'JSD_6-8|2': 0.46103877946328925,\n",
       " 'JSD_6-8|4': 0.8044862460712345}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "REPORT_TYPE = (\n",
    "    (6,6,1),\n",
    "    (6,6,2),\n",
    "    (6,6,4),\n",
    "    (6,7,1),\n",
    "    (6,7,2),\n",
    "    (6,7,4),\n",
    "    (6,8,1),\n",
    "    (6,8,2),\n",
    "    (6,8,4),\n",
    ")\n",
    "\n",
    "def _bond_type_str(bond_type) -> str:\n",
    "    atom1, atom2, bond_category = bond_type\n",
    "    return f'{atom1}-{atom2}|{bond_category}'\n",
    "\n",
    "def eval_bond_length_profile(model_profile):\n",
    "    metrics = {}\n",
    "\n",
    "    for bond_type in REPORT_TYPE:\n",
    "        metrics[f'JSD_{_bond_type_str(bond_type)}'] = sci_spatial.distance.jensenshannon(Globals.reference_bond_length_profile[bond_type],model_profile[bond_type])\n",
    "    return metrics\n",
    "\n",
    "eval_bond_length_profile(Globals.our_bond_length_profile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fc19b890",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'JSD_6-6|1': 0.6090847252592992,\n",
       " 'JSD_6-6|2': 0.6210207716132661,\n",
       " 'JSD_6-6|4': 0.4504618126310079,\n",
       " 'JSD_6-7|1': 0.4730538614136365,\n",
       " 'JSD_6-7|2': 0.6356224885267152,\n",
       " 'JSD_6-7|4': 0.5514678253305576,\n",
       " 'JSD_6-8|1': 0.49176438120726135,\n",
       " 'JSD_6-8|2': 0.5588741374756954,\n",
       " 'JSD_6-8|4': 0.810528953192532}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_bond_length_profile(Globals.ar_bond_length_profile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "aa76e593",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'JSD_6-6|1': 0.4959307399546982,\n",
       " 'JSD_6-6|2': 0.5614630763511905,\n",
       " 'JSD_6-6|4': 0.4158556788727053,\n",
       " 'JSD_6-7|1': 0.42529234081136563,\n",
       " 'JSD_6-7|2': 0.6285164082861112,\n",
       " 'JSD_6-7|4': 0.4873078388654103,\n",
       " 'JSD_6-8|1': 0.4540257178262056,\n",
       " 'JSD_6-8|2': 0.5160702401133311,\n",
       " 'JSD_6-8|4': 0.7828900804172967}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_bond_length_profile(Globals.pocket2mol_bond_length_profile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a3d21fee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'JSD_6-6|1': 0.6011662253516732,\n",
       " 'JSD_6-6|2': 0.6649823526244647,\n",
       " 'JSD_6-6|4': 0.49068619494120264,\n",
       " 'JSD_6-7|1': 0.6336144840358152,\n",
       " 'JSD_6-7|2': 0.7489228631212249,\n",
       " 'JSD_6-7|4': 0.638033543660596,\n",
       " 'JSD_6-8|1': 0.6559325575706507,\n",
       " 'JSD_6-8|2': 0.6609779429428257,\n",
       " 'JSD_6-8|4': 0.8325546111576978}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_bond_length_profile(Globals.cvae_bond_length_profile)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04e33a7a",
   "metadata": {},
   "source": [
    "# Rigid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d6793e41",
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "from rdkit.Chem.rdchem import BondType\n",
    "from copy import deepcopy\n",
    "from collections import OrderedDict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a832a1d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "class RotBondFragmentizer():\n",
    "    def __init__(self, only_single_bond=True):\n",
    "        self.type = 'RotBondFragmentizer'\n",
    "        self.only_single_bond = only_single_bond\n",
    "\n",
    "    # code adapt from Torsion Diffusion\n",
    "    def get_bonds(self, mol):\n",
    "        bonds = []\n",
    "        G = nx.Graph()\n",
    "        for i, atom in enumerate(mol.GetAtoms()):\n",
    "            G.add_node(i)\n",
    "        # nodes = set(G.nodes())\n",
    "        for bond in mol.GetBonds():\n",
    "            start, end = bond.GetBeginAtomIdx(), bond.GetEndAtomIdx()\n",
    "            G.add_edge(start, end)\n",
    "        for e in G.edges():\n",
    "            G2 = copy.deepcopy(G)\n",
    "            G2.remove_edge(*e)\n",
    "            if nx.is_connected(G2): continue\n",
    "            l = list(sorted(nx.connected_components(G2), key=len)[0])\n",
    "            if len(l) < 2: continue\n",
    "            # n0 = list(G2.neighbors(e[0]))\n",
    "            # n1 = list(G2.neighbors(e[1]))\n",
    "            if self.only_single_bond:\n",
    "                bond_type = mol.GetBondBetweenAtoms(e[0], e[1]).GetBondType()\n",
    "                if bond_type != BondType.SINGLE:\n",
    "                    continue\n",
    "            bonds.append((e[0], e[1]))\n",
    "        return bonds\n",
    "\n",
    "    def fragmentize(self, mol, dummyStart=1, bond_list=None):\n",
    "        if bond_list is None:\n",
    "            # get bonds need to be break\n",
    "            bonds = self.get_bonds(mol)\n",
    "        else:\n",
    "            bonds = bond_list\n",
    "        # whether the molecule can really be break\n",
    "        if len(bonds) != 0:\n",
    "            bond_ids = [mol.GetBondBetweenAtoms(x, y).GetIdx() for x, y in bonds]\n",
    "            bond_ids = list(set(bond_ids))\n",
    "            # break the bonds & set the dummy labels for the bonds\n",
    "            dummyLabels = [(i + dummyStart, i + dummyStart) for i in range(len(bond_ids))]\n",
    "            break_mol = Chem.FragmentOnBonds(mol, bond_ids, dummyLabels=dummyLabels)\n",
    "            dummyEnd = dummyStart + len(dummyLabels) - 1\n",
    "        else:\n",
    "            break_mol = mol\n",
    "            bond_ids = []\n",
    "            dummyEnd = dummyStart - 1\n",
    "\n",
    "        return break_mol, bond_ids, dummyEnd\n",
    "    \n",
    "\n",
    "def get_clean_mol(mol):\n",
    "    rdmol = deepcopy(mol)\n",
    "    for at in rdmol.GetAtoms():\n",
    "        at.SetAtomMapNum(0)\n",
    "        at.SetIsotope(0)\n",
    "    Chem.RemoveStereochemistry(rdmol)\n",
    "    return rdmol\n",
    "\n",
    "\n",
    "def replace_atom_in_mol(ori_mol, src_atom, dst_atom):\n",
    "    mol = deepcopy(ori_mol)\n",
    "    m_mol = Chem.RWMol(mol)\n",
    "    for atom in m_mol.GetAtoms():\n",
    "        if atom.GetAtomicNum() == src_atom:\n",
    "            atom_idx = atom.GetIdx()\n",
    "            m_mol.ReplaceAtom(atom_idx, Chem.Atom(dst_atom))\n",
    "    return m_mol.GetMol()\n",
    "\n",
    "\n",
    "def ff_optimize(ori_mol, addHs=False, enable_torsion=False):\n",
    "    mol = deepcopy(ori_mol)\n",
    "    Chem.GetSymmSSSR(mol)\n",
    "    if addHs:\n",
    "        mol = Chem.AddHs(mol, addCoords=True)\n",
    "    mp = AllChem.MMFFGetMoleculeProperties(mol, mmffVariant='MMFF94s')\n",
    "    if mp is None:\n",
    "        return (None, )\n",
    "\n",
    "    # turn off angle-related terms\n",
    "    mp.SetMMFFOopTerm(enable_torsion)\n",
    "    mp.SetMMFFAngleTerm(True)\n",
    "    mp.SetMMFFTorsionTerm(enable_torsion)\n",
    "\n",
    "    # optimize unrelated to angles\n",
    "    mp.SetMMFFStretchBendTerm(True)\n",
    "    mp.SetMMFFBondTerm(True)\n",
    "    mp.SetMMFFVdWTerm(True)\n",
    "    mp.SetMMFFEleTerm(True)\n",
    "    \n",
    "#     try:\n",
    "    ff = AllChem.MMFFGetMoleculeForceField(mol, mp)\n",
    "    energy_before_ff = ff.CalcEnergy()\n",
    "    ff.Minimize()\n",
    "    energy_after_ff = ff.CalcEnergy()\n",
    "    # print(f'Energy: {energy_before_ff} --> {energy_after_ff}')\n",
    "    energy_change = energy_before_ff - energy_after_ff\n",
    "    Chem.SanitizeMol(ori_mol)\n",
    "    Chem.SanitizeMol(mol)\n",
    "    rmsd = rdMolAlign.GetBestRMS(ori_mol, mol)\n",
    "#     except:\n",
    "#         return (None, )\n",
    "    return energy_change, rmsd, mol\n",
    "\n",
    "\n",
    "def frag_analysis_from_mol_list(input_mol_list):\n",
    "    all_frags_dict = {}\n",
    "    sg = RotBondFragmentizer()\n",
    "    for mol in tqdm(input_mol_list):\n",
    "        frags, _, _ = sg.fragmentize(mol)\n",
    "        frags = [get_clean_mol(f) for f in Chem.GetMolFrags(frags, asMols=True)]\n",
    "\n",
    "        for frag in frags:\n",
    "            num_atoms = frag.GetNumAtoms() - Chem.MolToSmiles(frag).count('*')\n",
    "            if 2 < num_atoms < 10:\n",
    "                if num_atoms not in all_frags_dict:\n",
    "                    all_frags_dict[num_atoms] = []\n",
    "\n",
    "                mol = deepcopy(frag)\n",
    "                mol_hs = replace_atom_in_mol(mol, src_atom=0, dst_atom=1)\n",
    "                mol_hs = Chem.RemoveAllHs(mol_hs)\n",
    "                all_frags_dict[num_atoms].append(mol_hs)\n",
    "    \n",
    "    all_frags_dict = OrderedDict(sorted(all_frags_dict.items()))\n",
    "    all_rmsd_by_frag_size = {}\n",
    "    for k, mol_list in all_frags_dict.items():\n",
    "        n_fail = 0\n",
    "        all_energy_diff, all_rmsd = [], []\n",
    "        for mol in mol_list:\n",
    "            ff_results = ff_optimize(mol, addHs=True, enable_torsion=False)\n",
    "            if ff_results[0] is None:\n",
    "                n_fail += 1\n",
    "                continue\n",
    "            energy_diff, rmsd, _, = ff_results\n",
    "            all_energy_diff.append(energy_diff)\n",
    "            all_rmsd.append(rmsd)\n",
    "        print(f'Num of atoms: {k} ({n_fail} of {len(mol_list)} fail):   '\n",
    "              f'\\tEnergy {np.mean(all_energy_diff):.2f} / {np.median(all_energy_diff):.2f}' \n",
    "              f'\\tRMSD   {np.mean(all_rmsd):.2f} / {np.median(all_rmsd):.2f}'\n",
    "             )\n",
    "        all_rmsd_by_frag_size[k] = all_rmsd\n",
    "    return all_frags_dict, all_rmsd_by_frag_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2ec19a6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "targetdiff_mols = [r['mol'] for pr in Globals.our_results for r in pr]\n",
    "ar_mols = [r['mol'] for pr in Globals.ar_results for r in pr]\n",
    "pocket2mol_mols = [r['mol'] for pr in Globals.pocket2mol_results for r in pr]\n",
    "cvae_mols = [r['mol'] for pr in Globals.cvae_results for r in pr]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fc50c262",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1ec467459c74484e86d22367dd6ef506",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/9036 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num of atoms: 3 (0 of 6413 fail):   \tEnergy 38.45 / 7.31\tRMSD   0.06 / 0.05\n",
      "Num of atoms: 4 (2 of 1331 fail):   \tEnergy 77.99 / 30.57\tRMSD   0.10 / 0.09\n",
      "Num of atoms: 5 (12 of 894 fail):   \tEnergy 22.38 / 18.01\tRMSD   0.12 / 0.11\n",
      "Num of atoms: 6 (0 of 2871 fail):   \tEnergy 142.97 / 11.98\tRMSD   0.12 / 0.09\n",
      "Num of atoms: 7 (0 of 2360 fail):   \tEnergy 3700.58 / 28.92\tRMSD   0.22 / 0.21\n",
      "Num of atoms: 8 (1 of 1288 fail):   \tEnergy 8560.93 / 50.42\tRMSD   0.32 / 0.30\n",
      "Num of atoms: 9 (0 of 903 fail):   \tEnergy 4447.17 / 62.52\tRMSD   0.38 / 0.34\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b74860c7868e412cbe5d225df6b8d241",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/9295 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num of atoms: 3 (0 of 4874 fail):   \tEnergy 34.78 / 25.79\tRMSD   0.09 / 0.07\n",
      "Num of atoms: 4 (0 of 1769 fail):   \tEnergy 128.49 / 73.06\tRMSD   0.18 / 0.16\n",
      "Num of atoms: 5 (1 of 828 fail):   \tEnergy 41.54 / 23.89\tRMSD   0.15 / 0.12\n",
      "Num of atoms: 6 (0 of 2421 fail):   \tEnergy 46.71 / 30.42\tRMSD   0.20 / 0.18\n",
      "Num of atoms: 7 (0 of 2039 fail):   \tEnergy 79.66 / 56.47\tRMSD   0.28 / 0.26\n",
      "Num of atoms: 8 (0 of 1296 fail):   \tEnergy 115.36 / 76.50\tRMSD   0.35 / 0.33\n",
      "Num of atoms: 9 (0 of 852 fail):   \tEnergy 169.01 / 117.29\tRMSD   0.39 / 0.36\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d410a6270b1247d99f62029383623ea7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/9831 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num of atoms: 3 (0 of 3626 fail):   \tEnergy 15.32 / 10.43\tRMSD   0.07 / 0.06\n",
      "Num of atoms: 4 (0 of 328 fail):   \tEnergy 38.03 / 33.93\tRMSD   0.11 / 0.10\n",
      "Num of atoms: 5 (0 of 762 fail):   \tEnergy 40.84 / 34.47\tRMSD   0.13 / 0.12\n",
      "Num of atoms: 6 (0 of 2138 fail):   \tEnergy 37.52 / 27.86\tRMSD   0.12 / 0.10\n",
      "Num of atoms: 7 (0 of 1673 fail):   \tEnergy 41.21 / 33.90\tRMSD   0.20 / 0.20\n",
      "Num of atoms: 8 (0 of 882 fail):   \tEnergy 51.09 / 42.97\tRMSD   0.26 / 0.24\n",
      "Num of atoms: 9 (0 of 708 fail):   \tEnergy 71.18 / 59.04\tRMSD   0.31 / 0.29\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "580eab5d2b7b42148a0d21fd95b3a685",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/9911 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num of atoms: 3 (80 of 9000 fail):   \tEnergy 287.29 / 86.32\tRMSD   0.22 / 0.19\n",
      "Num of atoms: 4 (205 of 2617 fail):   \tEnergy 398.41 / 165.15\tRMSD   0.31 / 0.28\n",
      "Num of atoms: 5 (399 of 3485 fail):   \tEnergy 246.44 / 105.96\tRMSD   0.26 / 0.21\n",
      "Num of atoms: 6 (22 of 2853 fail):   \tEnergy 301.55 / 185.70\tRMSD   0.34 / 0.32\n",
      "Num of atoms: 7 (0 of 1833 fail):   \tEnergy 485.96 / 243.79\tRMSD   0.40 / 0.37\n",
      "Num of atoms: 8 (2 of 1198 fail):   \tEnergy 2109.84 / 332.81\tRMSD   0.47 / 0.45\n",
      "Num of atoms: 9 (1 of 869 fail):   \tEnergy 1381.31 / 544.48\tRMSD   0.60 / 0.59\n"
     ]
    }
   ],
   "source": [
    "_, ours_rigid_rmsd = frag_analysis_from_mol_list(targetdiff_mols)\n",
    "_, ar_rigid_rmsd = frag_analysis_from_mol_list(ar_mols)\n",
    "_, pocket2mol_rigid_rmsd = frag_analysis_from_mol_list(pocket2mol_mols)\n",
    "_, cvae_rigid_rmsd = frag_analysis_from_mol_list(cvae_mols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "aedcea06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def construct_df(rigid_dict):\n",
    "    df = []\n",
    "    for k, all_v in rigid_dict.items():\n",
    "        for v in all_v:\n",
    "            df.append({'f_size': k, 'rmsd': v})\n",
    "    return pd.DataFrame(df)\n",
    "\n",
    "# sns.set(style=\"darkgrid\")\n",
    "sns.set_style(\"white\")\n",
    "sns.set_palette(\"muted\")\n",
    "\n",
    "tmp_1 = construct_df(ours_rigid_rmsd)\n",
    "tmp_1['model'] = MODEL_NAME\n",
    "tmp_2 = construct_df(ar_rigid_rmsd)\n",
    "tmp_2['model'] = 'AR'\n",
    "tmp_3 = construct_df(pocket2mol_rigid_rmsd)\n",
    "tmp_3['model'] = 'Pocket2Mol'\n",
    "tmp_4 = construct_df(cvae_rigid_rmsd)\n",
    "tmp_4['model'] = 'liGAN'\n",
    "\n",
    "viz_df = pd.concat([tmp_1, tmp_2, tmp_3, tmp_4]).reset_index()\n",
    "viz_df = viz_df.query('3<=f_size<=9')\n",
    "\n",
    "LABEL_FONTSIZE = 24\n",
    "TICK_FONTSIZE = 16\n",
    "LEGEND_FONTSIZE = 20\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.boxplot(x='f_size', y='rmsd', hue='model', data=viz_df, hue_order=('liGAN', 'AR', 'Pocket2Mol', MODEL_NAME), showfliers = False)\n",
    "plt.xlabel('Fragment Size', fontsize=LABEL_FONTSIZE)\n",
    "plt.ylabel('Median RMSD ($\\AA{}$)', fontsize=LABEL_FONTSIZE)\n",
    "plt.xticks(fontsize=TICK_FONTSIZE)\n",
    "plt.yticks(fontsize=TICK_FONTSIZE)\n",
    "plt.legend(frameon=False, fontsize=LEGEND_FONTSIZE)\n",
    "# plt.savefig('output_figures/rigid_rmsd.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a8ea35b",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Vina Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "35e0724d-a365-4667-a24e-f652f26a3c80",
   "metadata": {},
   "outputs": [],
   "source": [
    "our_vina = np.array([np.median([v['vina']['dock'][0]['affinity'] for v in pocket]) for pocket in Globals.our_results])\n",
    "ar_vina = np.array([np.median([v['vina']['dock'][0]['affinity'] for v in pocket]) if len(pocket) > 0 else 0. for pocket in Globals.ar_results])\n",
    "pocket2mol_vina = np.array([np.median([v['vina']['dock'][0]['affinity'] for v in pocket]) for pocket in Globals.pocket2mol_results])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2f787531",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_vina = np.stack([our_vina, ar_vina, pocket2mol_vina], axis=0)\n",
    "best_vina_idx = np.argmin(all_vina, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "77844cb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25, 6), dpi=100)\n",
    "\n",
    "ax = plt.subplot(1, 1, 1)\n",
    "ax.set_prop_cycle('color', plt.cm.Set1.colors)\n",
    "n_data = len(our_vina)\n",
    "fig1_idx = np.argsort(our_vina)\n",
    "ALPHA = 0.75\n",
    "POINT_SIZE = 128\n",
    "plt.scatter(np.arange(n_data), ar_vina[fig1_idx], label=f'AR (lowest in {np.mean(best_vina_idx==1)*100:.0f}%)', alpha=ALPHA, s=POINT_SIZE * 0.75)\n",
    "plt.scatter(np.arange(n_data), pocket2mol_vina[fig1_idx], label=f'Pocket2Mol (lowest in {np.mean(best_vina_idx==2)*100:.0f}%)', alpha=ALPHA, s=POINT_SIZE * 0.75)\n",
    "plt.scatter(np.arange(n_data), our_vina[fig1_idx], label=f'{MODEL_NAME} (lowest in {np.mean(best_vina_idx==0)*100:.0f}%)', alpha=ALPHA, s=POINT_SIZE)\n",
    "\n",
    "# plt.xticks([])\n",
    "plt.yticks(fontsize=16)\n",
    "for i in range(n_data):\n",
    "    plt.axvline(i, c='0.1', lw=0.2)\n",
    "plt.xlim(-1, 100)\n",
    "plt.ylim(-13, -1.5)\n",
    "# plt.yticks([-10, -8, -6, -4, -2], [-10, -8, -6, -4, '$\\geq$-2'], fontsize=25)\n",
    "plt.yticks([-12, -10, -8, -6, -4, -2], [-12, -10, -8, -6, -4, -2], fontsize=25)\n",
    "plt.ylabel('Median Vina Energy', fontsize=30)\n",
    "plt.legend(fontsize=25, loc='lower center', ncol=4, bbox_to_anchor=(0.5, -0.3), frameon=False)\n",
    "plt.xticks(np.arange(0, 100, 10), [f'target {v}' for v in np.arange(0, 100, 10)], fontsize=25)\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig('output_figures/binding.png')\n",
    "plt.show()"
   ]
  }
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